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Spearman Correlation of Models

Summary of 5_Default_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: Logloss
- eval_metric: AUC
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
7.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.69495 |
nan |
| auc |
0.55687 |
nan |
| f1 |
0.657266 |
0.120183 |
| accuracy |
0.548716 |
0.502599 |
| precision |
0.578947 |
0.681924 |
| recall |
1 |
0.120183 |
| mcc |
0.0962618 |
0.460723 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.69495 |
nan |
| auc |
0.55687 |
nan |
| f1 |
0.520012 |
0.502599 |
| accuracy |
0.548716 |
0.502599 |
| precision |
0.542395 |
0.502599 |
| recall |
0.499404 |
0.502599 |
| mcc |
0.0958501 |
0.502599 |
Confusion matrix (at threshold=0.502599)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1043 |
707 |
| Labeled as 1 |
840 |
838 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.692927 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.657266 |
0.440414 |
| accuracy |
0.489498 |
0.440414 |
| precision |
0.489498 |
0.440414 |
| recall |
1 |
0.440414 |
| mcc |
0 |
0.440414 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692927 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.657266 |
0.440414 |
| accuracy |
0.489498 |
0.440414 |
| precision |
0.489498 |
0.440414 |
| recall |
1 |
0.440414 |
| mcc |
0 |
0.440414 |
Confusion matrix (at threshold=0.440414)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
1750 |
| Labeled as 1 |
0 |
1678 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 3_Default_LightGBM |
1 |
| 4_Default_Xgboost |
1 |
| 5_Default_CatBoost |
1 |
Metric details
|
score |
threshold |
| logloss |
0.68698 |
nan |
| auc |
0.566304 |
nan |
| f1 |
0.657266 |
0.244451 |
| accuracy |
0.551925 |
0.511812 |
| precision |
0.646341 |
0.639755 |
| recall |
1 |
0.244451 |
| mcc |
0.104373 |
0.443417 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.68698 |
nan |
| auc |
0.566304 |
nan |
| f1 |
0.497711 |
0.511812 |
| accuracy |
0.551925 |
0.511812 |
| precision |
0.551449 |
0.511812 |
| recall |
0.453516 |
0.511812 |
| mcc |
0.10173 |
0.511812 |
Confusion matrix (at threshold=0.511812)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1131 |
619 |
| Labeled as 1 |
917 |
761 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
7.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.694754 |
nan |
| auc |
0.51616 |
nan |
| f1 |
0.657266 |
0.31903 |
| accuracy |
0.517211 |
0.529372 |
| precision |
0.621622 |
0.535765 |
| recall |
1 |
0.31903 |
| mcc |
0.0281982 |
0.465613 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.694754 |
nan |
| auc |
0.51616 |
nan |
| f1 |
0.378053 |
0.529372 |
| accuracy |
0.517211 |
0.529372 |
| precision |
0.511699 |
0.529372 |
| recall |
0.299762 |
0.529372 |
| mcc |
0.0281597 |
0.529372 |
Confusion matrix (at threshold=0.529372)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1270 |
480 |
| Labeled as 1 |
1175 |
503 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 3_Default_LightGBM
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LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: auc
- custom_eval_metric_name: None
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
2.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.690965 |
nan |
| auc |
0.5369 |
nan |
| f1 |
0.657266 |
0.344372 |
| accuracy |
0.541132 |
0.501621 |
| precision |
0.614907 |
0.533716 |
| recall |
1 |
0.344372 |
| mcc |
0.0801263 |
0.501621 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.690965 |
nan |
| auc |
0.5369 |
nan |
| f1 |
0.431925 |
0.501621 |
| accuracy |
0.541132 |
0.501621 |
| precision |
0.548121 |
0.501621 |
| recall |
0.356377 |
0.501621 |
| mcc |
0.0801263 |
0.501621 |
Confusion matrix (at threshold=0.501621)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1257 |
493 |
| Labeled as 1 |
1080 |
598 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 6_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
8.5 seconds
Metric details
|
score |
threshold |
| logloss |
1.1384 |
nan |
| auc |
0.515371 |
nan |
| f1 |
0.657266 |
0.350824 |
| accuracy |
0.523337 |
0.389805 |
| precision |
0.552381 |
0.694902 |
| recall |
1 |
0.350824 |
| mcc |
0.0470049 |
0.694902 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
1.1384 |
nan |
| auc |
0.515371 |
nan |
| f1 |
0.228517 |
0.389805 |
| accuracy |
0.523337 |
0.389805 |
| precision |
0.55 |
0.389805 |
| recall |
0.144219 |
0.389805 |
| mcc |
0.046444 |
0.389805 |
Confusion matrix (at threshold=0.389805)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1552 |
198 |
| Labeled as 1 |
1436 |
242 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 7_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
13.8 seconds
Metric details
|
score |
threshold |
| logloss |
0.691282 |
nan |
| auc |
0.533887 |
nan |
| f1 |
0.657266 |
0.379034 |
| accuracy |
0.53063 |
0.496192 |
| precision |
0.545156 |
0.509073 |
| recall |
1 |
0.379034 |
| mcc |
0.0579182 |
0.496192 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.691282 |
nan |
| auc |
0.533887 |
nan |
| f1 |
0.459886 |
0.496192 |
| accuracy |
0.53063 |
0.496192 |
| precision |
0.526518 |
0.496192 |
| recall |
0.408224 |
0.496192 |
| mcc |
0.0579182 |
0.496192 |
Confusion matrix (at threshold=0.496192)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1134 |
616 |
| Labeled as 1 |
993 |
685 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
61.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.707584 |
nan |
| auc |
0.560548 |
nan |
| f1 |
0.657266 |
0.0820577 |
| accuracy |
0.54755 |
0.572514 |
| precision |
0.653846 |
0.773927 |
| recall |
1 |
0.0820577 |
| mcc |
0.0945318 |
0.572514 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.707584 |
nan |
| auc |
0.560548 |
nan |
| f1 |
0.428308 |
0.572514 |
| accuracy |
0.54755 |
0.572514 |
| precision |
0.561353 |
0.572514 |
| recall |
0.346246 |
0.572514 |
| mcc |
0.0945318 |
0.572514 |
Confusion matrix (at threshold=0.572514)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
1296 |
454 |
| Labeled as 1 |
1097 |
581 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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